Method and apparatus for health assessment of a transport apparatus

ABSTRACT

A method for health assessment of a system including a transport apparatus including registering predetermined operating data embodying at least one dynamic performance variable output by the transport apparatus, determining a base value (CpkBase) characterized by a probability density function of each of the dynamic performance variable output, resolving from the transport apparatus in situ process motion commands of the apparatus controller and defining another predetermined motion set of the transport apparatus, registering predetermined operating data embodying the at least one dynamic performance variable output by the transport apparatus and determining with the processor another value (CpkOther) characterized by the probability density function of each of the dynamic performance variable output by the transport apparatus, and comparing the other value and the base value (CpkBase) for each of the dynamic performance variable output by the transport apparatus respectively corresponding to the predetermined motion base set and the other predetermined motion set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Non-Provisional patent application is a continuation of Ser. No.17/103,243, filed Nov. 24, 2020, (Now U.S. Pat. No. 11,338,437), whichis a continuation of Ser. No. 15/971,827, filed May 4, 2018, (Now U.S.Pat. No. 10,843,341), which claims priority to and the benefit of U.S.Provisional Patent Application No. 62/502,292, filed May 5, 2017, thedisclosure of which is incorporated herein by reference in its entirety.

BACKGROUND 1. Field

The exemplary embodiments generally relate to automated processingsystems, more particularly, to health assessment and predictivediagnostics of the automated processing systems.

2. Brief Description of Related Developments

Material damage and unscheduled downtime due to failures of roboticmanipulators and other mechatronic devices used in automatedmanufacturing tools, such as robotized material-handling platforms forproduction of semiconductor devices, are common problems which oftenrepresent a significant cost burden to the end-user of the manufacturingtools.

A number of health-monitoring and fault-diagnostic (HMFD) methods havebeen developed for industrial, automotive and aerospace applications.The existing systems typically implement fault detection to indicatethat something is wrong in the monitored system, fault isolation todetermine the exact location of the fault, i.e., the component which isfaulty, and fault identification to determine the magnitude of thefault.

The isolation and identification tasks together are often referred to asfault diagnosis. Many existing systems implement only the faultdetection and isolation stages.

Such fault diagnosis schemes, though helpful in the detection of faults,isolation thereof and adaptive recovery, nonetheless leave the device,tool, FAB (e.g. fabrication facility/plant), or other automatedequipment to operate in a substantially responsive manner with a limitedor substantially non-existent prediction horizon. Predictive methods areknown that seek to increase the prediction horizon to fault diagnosticsystems, such as mathematic modelling of the automated equipment, inwhich sensory measurements of the automated equipment variables arecompared to analytically computed values of the respective variables(generated, e.g., from Newtonian dynamic models of the automatedequipment, or neural network dynamic models), there the mathematicmodels represent nominal conditions. Such methods suffer fromnon-conservative factors, such as signal noise and modelling errors,that unpredictably and adversely influence the resulting differencebetween analytic (nominal) values and those from sensory measurements,and demand further investment by the fault diagnostic system inprocessing capacity and/or duplicative/redundant sensory systems anddata systems to resolve such non-conservative factors.

It would be advantageous to have a fault diagnostic system that providesprediction of faults without the non-conservative factors associatedmathematical modelling.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing aspects and other features of the disclosed embodiment areexplained in the following description, taken in connection with theaccompanying drawings, wherein:

FIG. 1 is a schematic illustration of a controller for an automatedapparatus, such as an automated material handling platform, inaccordance with aspects of the disclosed embodiment;

FIG. 2 is a schematic illustration of the automated material handlingplatform in accordance with aspects of the disclosed embodiment;

FIG. 2A is a schematic illustration of a system including a number ofdifferent unique apparatus in accordance with aspects of the disclosedembodiment;

FIG. 3 is a schematic illustration of an apparatus, such as a transportrobot, of the automated material handling apparatus in accordance withaspects of the disclosed embodiment;

FIGS. 4A-4E are schematic illustrations of different arm configurationsfor the apparatus of FIG. 3 in accordance with aspects of the disclosedembodiment;

FIG. 5A is a schematic illustration of a portion of the automatedmaterial handling platform showing base moves and in situ process movesin accordance with aspects of the disclosed embodiment;

FIGS. 5B and 5C are schematic illustrations of simple and complex movesin accordance with aspects of the disclosed embodiment;

FIG. 6 is an exemplary chart illustrating statistical convergence of asample of moves performed by the apparatus of FIG. 3 in accordance withaspects of the disclosed embodiment;

FIG. 7 is an exemplary move histogram in accordance with aspects of thedisclose embodiment;

FIGS. 8A1 and 8A2 (collectively referred to as FIG. 8A) are a schematicillustration of an exemplary process flow in accordance with aspects ofthe disclosed embodiment;

FIG. 8B is a schematic illustration of a portion of the exemplaryprocess flow of FIG. 8A in accordance with aspects of the disclosedembodiment;

FIG. 9 is an exemplary Gaussian distribution of sample moves indicatingupper and lower limits in accordance with aspects of the disclosedembodiment;

FIG. 10 is a graphic illustration of a comparison between a baselinevalue and another value generated from in situ process moves inaccordance with aspects of the disclosed embodiment;

FIG. 11 is an exemplary illustration of an application of a healthassessment of the apparatus of FIG. 3 with respect to predictivediagnostics in accordance with aspects of the disclosed embodiment;

FIG. 12 is an exemplary illustration of a health assessment indicationin accordance with aspects of the disclosed embodiment;

FIG. 13 is an exemplary flow diagram in accordance with aspects of thedisclosed embodiment;

FIG. 14 is a flow diagram in accordance with aspects of the disclosedembodiment; and

FIG. 15 is a flow diagram in accordance with aspects of the disclosedembodiment.

DETAILED DESCRIPTION

Although the aspects of the disclosed embodiment will be described withreference to the drawings, it should be understood that the aspects ofthe disclosed embodiment can be embodied in many forms. In addition, anysuitable size, shape or type of elements or materials could be used.

The aspects of the disclosed embodiment described herein provide amethod and apparatus for quantifying the health status of and predictivediagnostics for an automated system (such as those descried herein withrespect to FIGS. 1-4E) using available variables being monitored by anysuitable controller of the automated system (where the controllerincludes non-transitory computer software code for implementing theaspects of the disclosed embodiment). The metrics of the health statusis achieved with the aspects of the disclosed embodiments by a uniquestatistical data treatment for the variables collected that uniquelycorrelate to and characterize uniquely for a given apparatus and/orsystem of several apparatus a health status quantity providingpredictive diagnosis of the given apparatus and/or system. The aspectsof the disclosed embodiment may allow for the controller of theautomated system to determine statistical signatures (uniquelycharacteristic of a unique device) of the monitored variables (of theunique device) using the concept of “baselines” (which include basevalues and/or base motions as described herein) and further morecomparing future performances against such baselines. As a result, themethod and apparatus of the disclosed embodiment may allow for thecontroller of the automated system to perform predictions based ontrending analysis, allowing the controller of the automated system tomake recommendations for preventive maintenance based on data that isunique from the automated system being monitored. The aspects of thedisclosed embodiment may also allow for identification of an expectedlimit of operation for variables that are difficult to determineacceptable and practical specifications.

While the aspects of the disclosed embodiment will be described hereinwith respect to a semiconductor robot (also referred to herein as arobotic manipulator) having three degrees of freedom (theta rotation, Rextension and Z lift motion); in other aspects the semiconductor robotmay have more or less than three degrees of freedom. In still otheraspects, the disclosed embodiment can be applied to other components ofa semiconductor processing system having a single degree of freedom ofmotion (such as robotic transports, load ports, aligners, pumps, fans,valves, etc.). It should also be understood that the aspects of thedisclosed embodiment may be used for any automated and/or powereddevice(s) or system (including, such as, a combination of aforementionedapparatus and/or devices) that is capable of sampling similar or relatedperformance monitoring data that uniquely correlates to and uniquelycharacterizes each unique apparatus, device and/or system.

The aspects of the disclosed embodiment provide a type of metrics thatis normalized based on statistical parameters which allows for a directcomparison of variables of different physical meaning, such astemperature versus peak torque. Such comparison allows for thecomputation of the impact of such unrelated variables on the overallhealth status of the automated system being monitored.

FIG. 1 shows an exemplary controller 100 for an automated apparatusincorporating automated apparatus health assessment and predictivediagnostics in accordance with the aspects of the disclosed embodiment.The aspects of the disclosed embodiment may operate in hardware orsoftware. For example, the aspects of the disclosed embodiment mayreside in a component controller, a controller that directs theoperation of a number of components, a controller that controls asub-system, or a system controller. The aspects of the disclosedembodiment may also be implemented in dedicated hardware or software.

The controller 100 is any suitable controller of an automated apparatus(such as the automated material handling platform 300 illustrated inFIG. 3 ) and may generally include a processor 105, read only memory110, random access memory 115, program storage 120, a user interface125, and a network interface 130. Processor 105 may include an on boardcache 135 and is generally operable to read information and programsfrom a computer program product, for example, a computer useable medium,such as on board cache 135, read only memory 110, random access memory115, and program storage 120.

Upon power up, processor 105 may begin operating programs found in readonly memory 110 and after initialization, may load instructions fromprogram storage 120 to random access memory 115 and operate undercontrol of those programs. Frequently used instructions may betemporarily stored in on board cache 135. Both read only memory 110 andrandom access memory 115 may utilize semiconductor technology or anyother appropriate materials and techniques. Program storage 120 mayinclude a diskette, a computer hard drive, a compact disk (CD), adigital versatile disk (DVD), an optical disk, a chip, a semiconductor,or any other device capable of storing programs in the form of computerreadable code.

On board cache 135, read only memory 110, random access memory 115, andprogram storage 120, either individually or in any combination mayinclude operating system programs. The operating system programs may besupplemented with an optional real time operating system to improve thequality of data provided by the function controller 100 and to allow thefunction controller 100 to provide a guaranteed response time.

In particular, on board cache 135, read only memory 110, random accessmemory 115, and program storage 120, either individually or in anycombination may include programs for causing the processor 105 toperform fault diagnostics and fault prediction in accordance with theaspects of the disclosed embodiment as described herein. Networkinterface 130 may be generally adapted to provide an interface betweenthe controller 100 and other controllers or other systems. Networkinterface 130 may operate to receive data from one or more additionalfunction controllers and to convey data to the same or other functioncontrollers. Network interface 130 may also provide an interface to aglobal diagnostic system that may provide remote monitoring anddiagnostic services.

Communication network 190 may include the Public Switched TelephoneNetwork (PSTN), the Internet, a wireless network, a wired network, aLocal Area Network (LAN), a Wide Area Network (WAN), a virtual privatenetwork (VPN) etc., and may further include other types of networksincluding X.25, TCP/IP, ATM, etc.

The controller 100 may include a user interface 125 with a display 140and an input device such as a keyboard 155 or mouse 145. The userinterface may be operated by a user interface controller 150 undercontrol of processor 105 and may provide a user with a graphical userinterface to visualize the results of the health monitoring and faultdiagnostics. The user interface may also be used to guide servicepersonnel through troubleshooting routines or repair processes. Inaddition, the user interface controller may also provide a connection orinterface 155 for communicating with other function controllers, anexternal network, another control system, or a host computer.

An exemplary material-handling platform for production of semiconductordevices in which the aspects of the disclosed embodiment may beimplemented is depicted diagrammatically in FIG. 2 , with explanatorynotes for major components being listed in Table 1. One or morecontrollers of the material-handling platform of FIG. 2 may include acontroller as described herein with respect to FIG. 1 .

TABLE 1 Explanatory notes for the automated material handling platform300 (also referred to as a process tool) of FIG. 2. Number Description301 Atmospheric section 302 Vacuum section 303 Process module 304Enclosure 305 Loadport 306 Atmospheric robotic manipulator 307 Substratealigner 308 Fan-filter unit 309 Vacuum chamber 310 Load-lock 311 Vacuumrobotic manipulator 312 Vacuum pump 313 Slit valve 314 Tool controller315 Atmospheric section controller 316 Vacuum section controller 317Process controller 318 Loadport controller 319 Atmospheric robotcontroller 320 Aligner controller 321 Fan-filter unit controller 322Motor controller 323 Vacuum robot controller

The automated material handling platform 300 has an atmospheric section301, vacuum section 302 and one or multiple process modules 303.

The atmospheric section 301 may include an enclosure 304, one ormultiple loadports 305, one or multiple robotic manipulators 306, one ormultiple substrate aligners 307 and a fan-filter unit 308. It may alsoinclude one or more ionization units (not shown). The vacuum section mayinclude a vacuum chamber 309, one or multiple load-locks 310, one ormultiple robotic manipulators 311, one or multiple vacuum pumps 312 anda plurality of slit valves 313, which are typically located at theinterface of the atmospheric section 301 with the load-locks 310,between the load-locks 310 and the vacuum chamber 309, and between thevacuum chamber 309 and the process module 303.

The operation of the platform is coordinated by the tool controller 314,which supervises the atmospheric section controller 315, vacuum sectioncontroller 316 and one or multiple process controllers 317. Theatmospheric section controller 315 is in charge of one or multipleloadport controllers 318, one or multiple atmospheric robot controllers319, one or multiple aligner controllers 320 and a fan-filter unitcontroller 321. Each of the loadport controllers 318, atmospheric robotcontrollers 319 and aligner controllers 320 is in turn in charge of oneor multiple motor controllers 322. The vacuum section controller 316 isin charge of one or multiple vacuum robot controllers 323, controls thevacuum pump 312 and operates the slit valves 313. The role of theprocess controller 317 depends on the operations performed in theprocess modules 303.

In some cases, it may be practical to combine two or more layers ofcontrol into a single controller. For instance, the atmospheric robotcontroller 319 and the corresponding motor controllers 322 may becombined in a single centralized robot controller, or the atmosphericsection controller 315 can be combined with the atmospheric robotcontroller 319 to eliminate the need for two separate controller units.

A five-axis direct-drive robotic manipulator 400 may be employed in theautomated material handling platform 300 of FIG. 2 where one or more ofthe atmospheric robotic manipulator 306 and the vacuum roboticmanipulator 311 is/are substantially similar to the robotic manipulator400. A simplified schematic of such a robotic manipulator 400 isprovided in FIG. 3 . Explanatory notes for major components are listedin Table 2. In one aspect, the aspects of the disclosed embodiment maybe implemented within the robot manipulator 400; however, it should beunderstood that while the aspects of the disclosed embodiment aredescribed with respect to a robotic manipulator, the aspects of thedisclosed embodiment can be implemented in any suitable automatedportion of the automated material handling platform 300 including butnot limited to transport robots, load ports, aligners, pumps, fans,valves, etc. noting that the controller 800 in FIG. 8A is a generalrepresentation of the controller for any of the aforementioned automatedequipment. It is noted that the robotic manipulator 400 is illustratedas a five-axis direct drive robotic manipulator for exemplary purposesonly and in other aspects the robotic manipulator (or other automatedportion of the process tool including the aspects of the disclosedembodiment) may have any suitable number of drive axes, with anysuitable number of degrees of freedom, and with a direct or indirectdrive system.

TABLE 2 Explanatory notes for robotic manipulator 400 of FIG. 3. NumberDescription 401 Robot frame 402 Mounting flange 403 Vertical rail 404Linear bearing 405 Carriage 406 Vertical drive motor 407 Ball screw 408Motor 1 (driving link 1) 409 Motor 2 (driving link 2) 410 Encoder 1(coupled to motor 1) 411 Encoder 2 (coupled to motor 2) 412 Outer shaft413 Inner shaft 414 Link 1 (upper arm) 415 Belt driving link 2 416 Link2 (forearm) 417A Motor A (driving end-effector A) 417B Motor B (drivingend-effector B) 418A First stage of belt drive A 418B First stage ofbelt drive B 419A Second stage of belt drive A 419B Second stage of beltdrive B 420A End-effector A (upper end-effector) 420B End-effector B(lower end-effector) 421A, 421B Payload on end-effectors A and B 422Master controller 423A, 423B, 423C Motor controllers 424A, 424BElectronic units for end-effectors A and B 425 Communications network426 Slip-ring 428A, 428B Mapper sensors 429 Power supply 430 Vacuum pump431A, 431B Valves 432A, 432B Pressure sensors 433, 434A, 434B Lip-seals435 Brake

Referring to FIG. 3 , the robotic manipulator 400 is built around anopen cylindrical frame 401 suspended from a circular mounting flange402. The frame 401 incorporates a vertical rail 403 with linear bearing404 to provide guidance to a carriage 405 driven by a brushless DC motor406 via a ball-screw mechanism 407. The carriage 405 houses a pair ofcoaxial brushless DC motors 408, 409 equipped with optical encoders 410,411. The upper motor 408 drives a hollow outer shaft 412 connected tothe first link 414 of the robot arm. The lower motor 409 is connected toa coaxial inner shaft 413 which is coupled via a belt drive 415 to thesecond link 416. The first link 414 houses a brushless DC motor 417Awhich drives through a two-stage belt arrangement 418A, 419A the upperend-effector 420A. Another DC brushless motor 417B and a two-stage beltdrive 418B, 419B are employed to actuate the lower end-effector 420B.Each of the stages 418A, 418B, 419A and 419B are designed with a 1:2ratio between the input and output pulleys. Substrates 421A and 421B areheld attached to end-effectors 420A and 420B, respectively, by the meansof vacuum-actuated edge-contact grippers, surface-contact suctiongrippers or passive grippers. The first link 414, second link 416, upperend-effector 420A and lower end-effector 420B are also referred to asthe upper arm, forearm, end-effector A and end-effector B, respectively,throughout the text. The points A, B and C indicate revolute couplingswhich are referred to as the shoulder, elbow and wrist joints,respectively. Point D denotes a reference point which indicates thedesired location of the center of the substrate on the correspondingend-effector.

The control system of the example robotic manipulator may be adistributed type. It comprises a power supply 429, master controller 422and motor controllers 423A, 423B and 423C. The master controller 422 isresponsible for supervisory tasks and trajectory planning. Each of themotor controllers 423A, 423B and 423C execute the position and currentfeedback loops for one or two motors. In FIG. 3 , the controller 423Acontrols motors 408 and 409, the controller 423B controls motors 417Aand 417B and the controller 423C controls motor 406. In addition toexecuting the feedback loops, the motor controllers also collect datasuch as motor current, motor position and motor velocity, and stream thedata to the master controller. The motor controllers 423A, 423B and 423Care connected to the master controller through a high-speedcommunication network 425. Since the joint A is an infinite rotationjoint, the communication network 425 is routed through a slip-ring 426.Additional electronic units 424A and 424B may be used to support theedge-contact grippers of the end-effectors 420A and 420B, respectively.

Referring now to FIGS. 4A-4E, the robotic manipulator 400 of FIG. 3 mayinclude any suitable arm linkage mechanism(s). Suitable examples of armlinkage mechanisms can be found in, for example, U.S. Pat. No. 7,578,649issued Aug. 25, 2009, U.S. Pat. No. 5,794,487 issued Aug. 18, 1998, U.S.Pat. No. 7,946,800 issued May 24, 2011, U.S. Pat. No. 6,485,250 issuedNov. 26, 2002, U.S. Pat. No. 7,891,935 issued Feb. 22, 2011, U.S. Pat.No. 8,419,341 issued Apr. 16, 2013 and U.S. patent application Ser. No.13/293,717 entitled “Dual Arm Robot” and filed on Nov. 10, 2011 and Ser.No. 13/861,693 entitled “Linear Vacuum Robot with Z Motion andArticulated Arm” and filed on Sep. 5, 2013 the disclosures of which areall incorporated by reference herein in their entireties. In aspects ofthe disclosed embodiment, the at least one transfer arm of eachtransport unit module 104, the boom arm 143 and/or the linear slide 144may be derived from a conventional SCARA arm 315 (selective compliantarticulated robot arm) (FIG. 4C) type design, which includes an upperarm 315U, a band-driven forearm 315F and a band-constrained end-effector315E, or from a telescoping arm or any other suitable arm design, suchas a Cartesian linearly sliding arm 314 (FIG. 4B). Suitable examples oftransport arms can be found in, for example, U.S. patent applicationSer. No. 12/117,415 entitled “Substrate Transport Apparatus withMultiple Movable Arms Utilizing a Mechanical Switch Mechanism” filed onMay 8, 2008 and U.S. Pat. No. 7,648,327 issued on January 19, 100G, thedisclosures of which are incorporated by reference herein in theirentireties. The operation of the transfer arms may be independent fromeach other (e.g. the extension/retraction of each arm is independentfrom other arms), may be operated through a lost motion switch or may beoperably linked in any suitable way such that the arms share at leastone common drive axis. In still other aspects the transport arms mayhave any other desired arrangement such as a frog-leg arm 316 (FIG. 4A)configuration, a leap frog arm 317 (FIG. 4E) configuration, abi-symmetric arm 318 (FIG. 4D) configuration, etc. Suitable examples oftransport arms can be found in U.S. Pat. No. 6,231,297 issued May 15,2001, U.S. Pat. No. 5,180,276 issued Jan. 19, 1993, U.S. Pat. No.6,464,448 issued Oct. 15, 2002, U.S. Pat. No. 6,224,319 issued May 1,2001, U.S. Pat. No. 5,447,409 issued Sep. 5, 1995, U.S. Pat. No.7,578,649 issued Aug. 25, 2009, U.S. Pat. No. 5,794,487 issued Aug. 18,1998, U.S. Pat. No. 7,946,800 issued May 24, 2011, U.S. Pat. No.6,485,250 issued Nov. 26, 2002, U.S. Pat. No. 7,891,935 issued Feb. 22,2011 and U.S. patent application Ser. No. 13/293,717 entitled “Dual ArmRobot” and filed on Nov. 10, 2011 and Ser. No. 13/270,844 entitled“Coaxial Drive Vacuum Robot” and filed on Oct. 11, 2011 the disclosuresof which are all incorporated by reference herein in their entireties.

Still referring to FIGS. 2-4E the robotic manipulators 306, 311, 400described herein transport substrates S (see FIGS. 4A and 4B) betweenpoints in space, such as substrate holding stations STN1-STN6illustrated in FIG. 5A. In order to accomplish the transport ofsubstrates S a motion control algorithm runs in any suitable controllerof the automated material handling platform 300, such as robotcontroller (also referred to as a robotic manipulator controller) 319,323, 422, 423A-423C, 810 (see FIGS. 2, 3 and 8A), that is connected tothe robotic manipulator 306, 311, 400. The motion control algorithmdefines the desired substrate path in space and a position control loopcalculates the desired control torques (or forces) to apply for eachrobot actuator that is responsible for moving each respective robotdegree-of-freedom in space.

The robotic manipulators 306, 311, 400 (which may be referred to asautomated systems) are expected to perform the repetitive task oftransferring substrates S continuously and the robotic manipulators aresubjected to the environmental conditions associated with the processingof such substrates. It is advantageous to have a method and apparatus,as provided by the aspects of the disclosed embodiment, to monitorrobotic manipulator (or any other automated equipment of the automatedmaterial handling platform 300) performance over time and determine(predictive diagnostics) if the respective robotic manipulator 306, 311,400 is able to operate within expected parameters in order to handle itsprimary task such as carrying and transferring the substrates betweensubstrate holding stations STN1-STN6.

In accordance with the aspects of the disclosed embodiment, the healthassessment of, for example, the robotic manipulator 306, 311, 400 isperformed by generating a base statistic signature (e.g. a baseline orstatistical representation of the behavior of a given variable operatingin typical environmental conditions) that characterizes each dynamicperformance variable output by the robotic manipulator 306, 311, 400 fora set of base moves/motions (the terms move and motion are usedinterchangeably herein) 820, 820A, 820B 820C (See FIG. 8A) of therobotic manipulator 306, 311, 400. The base statistic signature isgenerated by, for example, registering, with a registration system 801R(which may be formed by or resident in any suitable storage, such asstorage 801) communicably coupled to any suitable controller of theautomated material handling platform 300 such as controller 319, 323,422, 423A, 423B, 423C, 810, predetermined operating data embodying atleast one dynamic performance variable output by the robotic manipulator306, 311, 400 effecting a predetermined motion base set of predeterminedbase motions.

Each of the dynamic performance variables is specific to the automatedsystem (such as the robotic manipulator 306, 311, 400), which may be ina group of different automated systems (such as the group of automatedsystems that form the automated material handling platform 300) fromwhich dynamic performance variable was obtained. As such, because eachof the dynamic performance variables is specific to a respective one ofthe automated systems (of the group of automated systems) the basestatistic signature of the respective automated system travels with therespective automated system. For example, robotic manipulator 306located in the atmospheric section 301 of the automatedmaterial-handling platform 300 has a respective base statistic signatureand robotic manipulator 311 located in the vacuum section 302 has arespective base statistic signature. If the robotic manipulator 311 wasplaced in the atmospheric section 301, the base statistic signature ofthe robotic manipulator 311 would still apply to the robotic manipulator311 when placed within the atmospheric section 301. In one aspect, thebase statistic signature is associated with the respective automatedsystem in a memory and/or controller of the automated system. Further,each robotic manipulator may have unique operational characteristicsthat affect the base statistic signature of the respective roboticmanipulator. For example, robotic manipulator 311 and another roboticmanipulator may be manufactured as the same make and model roboticmanipulator. However, due to, for example, manufacturing tolerances inthe robotic drive systems and arm structures, the base statisticsignature for robotic manipulator 311 may not be applicable to the othersimilar robotic manipulator and vice versa. As such, the base statisticsignature for each robotic manipulator travels with the respectiverobotic manipulator (e.g. the base statistic signature C_(pkbase) forrobotic manipulator 311 travels with and is unique to roboticmanipulator 311 and the base statistic signature C_(pkbase) for roboticmanipulator 306 travels with and is unique to robotic manipulator 306).Accordingly each apparatus, such as robotic manipulator 311, is uniqueand each normalized value or base statistic signature/value C_(pkbase)for each predetermined base move 501, 502, 503 of the predeterminedmotion base set 820, 820A-820C and each other value C_(pkOther) for eachmapped in situ process move 501′, 502′, 503′ of the other predeterminedmotion set 830, 830A-830C are uniquely correlated with but the uniqueapparatus, and the determined performance deterioration rate (such asindicated by a linear trending model LTM—see FIG. 11 ) correlatesuniquely with but the unique apparatus.

In one aspect, a system (such as the automated material handlingplatform 300 illustrated in FIG. 3 ) includes or is otherwise providedwith a number of different unique apparatus (such as the aligner 307,robotic manipulator 306, fan filter unit 308, etc., listed in Table 1and illustrated in FIG. 2 ) connected to each other and, for example,the transport apparatus 311, wherein each different unique apparatusfrom the number of different unique apparatus App(i) (schematicallyrepresented in FIG. 2A as App1-Appn) has different correspondingnormalized values C_(pkBasei) (which includes C_(pkBase(1-n))) for eachbase move 501, 502, 503 of the predetermined motion base set 820,820A-820C and other normalized values C_(pkOtheri) for each mapped insitu process move 501′, 502′, 503′ of the other predetermined motion set830, 830A-830C that uniquely correlate to no more than that differentcorresponding unique apparatus App1-Appn from the number of differentunique apparatus App(i). In one aspect, each (or at least one) differentunique apparatus App1-Appn from the number of different unique apparatusApp(i) is of common configuration with another one of the differentunique apparatus App1-Appn. For example, robotic manipulator 306 mayhave a common configuration with robotic manipulator 311. In otheraspects, each (or at least one) different unique apparatus App1-Appnfrom the number of different unique apparatus App(i) is of differentconfiguration from another one of the different unique apparatus App(i).For example, the aligner 307 has a difference configuration than therobotic manipulator 306.

The dynamic performance variables of each automated apparatus and/orsystem can be directly measured (i.e. continuous monitoring variables)or derived from available measurements (i.e. derived variables).Examples of the dynamic performance variables include:

Mechanical or electrical power;

Mechanical work;

Robot end-effector acceleration;

Motor PWM duty: PWM duty of a motor is the percentage of input voltagethat is supplied to each motor phase at any given time. The duty cycleat each of the motor phases is available to the health-monitoring andfault-diagnostic system;

Motor current: Motor current represents the current flowing through eachof the three phases of each of the motors. The motor current may beobtained as an absolute value or as a percentage of the maximum current.If obtained as an absolute value it has units of Amps. Motor currentvalues can in turn be used to compute motor torques using the motortorque-current relationships;

Actual position, velocity and acceleration: These are the position,velocity and acceleration of each of the motor axes. For rotationalaxes, the position, velocity and acceleration values are in units ofdegrees, degrees/sec and degrees/sect respectively. For translationalaxes, the position, velocity and acceleration values are in units of mm,mm/sec and mm/sect respectively;

Desired position, velocity and acceleration: These are the position,velocity and acceleration values that the controller commands the motorsto have. These properties have similar units as the actual position,velocity and acceleration above;

Position and velocity tracking error: These are the differences betweenthe respective desired and actual values. These properties have similarunits as the actual position, velocity and acceleration above;

Settling time: This is the time it takes for the position and velocitytracking errors to settle within specified windows at the end of motion;

Encoder analog and absolute position outputs: The motor position isdetermined by the encoders which output two types of signals—analogsignals and absolute position signals. Analog signals are sine andcosine signals in units of mVolts. Absolute position signals arenon-volatile integer values that indicate the number of analog sinecycles or an integer multiple of analog sine cycles that have gone by.Typically, digital outputs are read on power up and thereafter the axisposition is determined solely from the analog signals;

Gripper state: This is the state of the gripper—open or closed. In thecase of a vacuum-actuated edge-contact gripper, it is theblocked/unblocked state of one or more sensors;

Vacuum system pressure: This is the vacuum level measured by a vacuumsensor. This is an analog sensor whose output is digitized by ananalog-to-digital converter. In the case of a suction gripper, thevacuum level indicates whether the wafer has been gripped;

Substrate-presence sensor state: In a passive grip end effector, thewafer presence sensor output is a binary output. In a vacuum-actuatededge-contact grip end effector, the wafer presence is determined fromthe output state of two or more sensors each of which is binary;

Mapper sensor state: This is the state of the mapper sensor—blocked orunblocked at any given instance;

Substrate Mapper/Aligner detector light intensity: This is a measure ofthe intensity of the light detected by a light detector of a substratemapper or aligner. This signal is typically available as an integervalue (that may have a range of 0-1024 as an example);

Substrate mapper sensor position capture data: This is the array ofrobot axis position values at which the mapper sensor changes state;

Vacuum valve state: This is the commanded state of the vacuum valve. Itspecifies if the solenoid that operates the vacuum valve is supposed tobe energized;

Voltage at fuse output terminals: The voltages at the output terminalsof each of the fuses in the motor control circuitry is monitored. Ablown fuse results in low output terminal voltage;

Substrate alignment data: These are the substrate eccentricity vectorand angular orientation of the alignment fiducial of a substratereported by the aligner;

Position data at transition of external substrate sensors: In somecases, the atmospheric and vacuum sections of the tool may be equippedwith optical sensors which detect the leading and trailing edges of asubstrate carried by the robot. The robot position data corresponding tothese events are used for on-the-fly recognition of the eccentricity ofthe substrate on the robot end-effector;

Substrate cycle time: This is the time it takes for a single substrateto be processed by the tool, typically measured under steady flowconditions;

Mini-environment pressure: This is the pressure measured by a pressuresensor in the atmospheric section of the tool.

Particular examples of the continuous monitoring variables include:

TABLE 3 Continuous monitoring variables Name Units_Short Units_Long T1position actual deg Degrees T2 position actual deg Degrees Z positionactual m Meters T1 velocity actual deg/sec Degrees per second T2velocity actual deg/sec Degrees per second Z velocity actual m/secMeters per second T1 acceleration actual deg/sec2 Degrees per secondsquared T2 acceleration actual deg/sec2 Degrees per second squared Zacceleration actual m/sec2 Meters per second squared T1 accelerationcommand deg/sec2 Degrees per second squared T2 acceleration commanddeg/sec2 Degrees per second squared Z acceleration command m/sec2Millimeters per second squared T1 position error deg Degrees T2 positionerror deg Degrees Z position error deg Degrees T1 torque actual NNewton-meters T2 torque actual N Newton-meters Z torque actual NNewton-meters T1 torque modeled N Newton-meters T2 torque modeled NNewton-meters Z torque modeled N Newton-meters T1 current actual A AmpsT2 current actual A Amps Z current actual A Amps Bus motor voltage VVolts Bus 24 V rail V Volts Bus 12 V rail V Volts Bus 5 V rail V VoltsBus 3.3 V rail V Volts Core temperature C. Celsius FPGA core supplyvoltage V Volts FPGA io supply voltage V Volts Processor core supply VVolts voltage Processor io supply V Volts voltage DDR supply voltage VVolts T1 temperature C. Celsius T2 temperature C. Celsius Z temperatureC. Celsius T1 servo Status bitwise Bitwise T2 servo Status bitwiseBitwise Z servo Status bitwise Bitwise T1 encoder CRC error countercount Counter T2 encoder CRC error counter count Counter Z encoder CRCerror counter count Counter T1 encoder warning flag count Countercounter T2 encoder warning flag count Counter counter Z encoder warningflag count Counter counter T1 encoder error flag count Counter counterT2 encoder error flag count Counter counter Z encoder error flag countCounter counter T1 command position deg Degrees T2 command position degDegrees Z command position m Meters Radial command position m MetersRadial command position arm B m Meters Theta command position degDegrees Theta command position arm B deg Degrees Radial position error mMeters Radial position error arm B m Meters Tangential position error mMeters Tangential position m Meters error arm B Command compoundacceleration g g Command compound acceleration g g arm B Actual compoundacceleration g g Actual compound acceleration g g arm B CPU core 0utilization % Percentage CPU core 1 utilization % Percentage Totalsystem RAM B Bytes Available system RAM B Bytes Used system RAM B BytesUsed percent system RAM % Percentage Total disk 0 space B BytesAvailable disk 0 space B Bytes Used disk 0 space B Bytes Used percentdisk 0 space % Percentage Processor PCB temperature C. Celsius RTCbattery low flag Bool Boolean Number of controller boot count Countcycles Controller hardware on time days Days Total cycle count countCount Fan controller internal C. Celsius temperature Fan 0 speed RPM RPMFan 0 error flag Bool Boolean Fan 1 speed RPM RPM Fan 1 error flag BoolBoolean

Where T1 and T2 are robotic manipulator drive rotation axes (there couldbe more or less than two rotation drive axes); Z is the robot drive Zaxis; CPU is the robot controller (such as controller 319, 323, 422,423A-423C, 800); Fan 0, Fan 1 are the various fans of the roboticmanipulator; theta is rotation of the robotic manipulator arm; and R isthe extension of the robotic manipulator arm.

Particular examples of derived variables include:

TABLE 4 Derived variables Name Units_Short Units_Long T1 servo statusduration days Days T2 servo status duration days Days Z servo statusduration days Days T1 servo status bool Boolean T2 servo status boolBoolean Z servo status bool Boolean T1 mechanical power W Watts T2mechanical power W Watts Z mechanical power W Watts T1 electrical powerW Watts T2 electrical power W Watts Z electrical power W Watts T1 motorefficiency % percentage T2 motor efficiency % percentage Z motorefficiency % percentage T1 incremental energy J Joules T2 incrementalenergy J Joules Z incremental energy J Joules T1 incremental positiondeg Degrees T2 incremental position deg Degrees Z incremental position mMeters Position loop average execution time us Microseconds Positionloop max execution time us Microseconds Position loop average signaldelay time us Microseconds Position loop max signal delay time usMicroseconds Current loop average execution time us Microseconds Currentloop max execution time us Microseconds IO loop average interval time msMilliseconds IO loop max interval time ms Milliseconds IO loop averageexecution time ms Milliseconds IO loop max execution time msMilliseconds T1 encoder CRC error flag count Counter T2 encoder CRCerror flag count Counter Z encoder CRC error flag count Counter T1encoder warning flag count Counter T2 encoder warning flag count CounterZ encoder warning flag count Counter T1 encoder error flag count CounterT2 encoder error flag count Counter Z encoder error flag count Counter

Such dynamic performance variables are calculated from raw or directmeasurements such as motor position, velocity, acceleration and controltorques.

The predetermined base moves 501, 502, 503 of the predetermined motionbase set 820, 820A-820C include a statistically characterizing number ofat least one common base move (e.g. a move that forms a baseline and iscreated from enough samples moves that are collected to define astatistically meaningful batch) defining a base motion type. Forexample, a (each) motion base set 820, 820A-820C (see FIG. 8A) for arespective base move 501, 502, 503 (e.g. base move 501 has the motionbase set 820A, base move 502 has the motion base set 820B, base move 503has the motion base set 820C) is substantially a minimum number of movesN_(min) (see FIG. 6 ) (e.g. sample size) sufficient to provide astatistically meaningful standard deviation based on a given convergencecriterion to characterize the motion base set (or move set) 820,820A-820C for a specific robotic manipulator 306, 311, 400. As such,each dynamic performance variable is specific to and output by arespective robotic manipulator 306, 311, 400.

The predetermined base moves 501, 502, 503, of the respectivepredetermined motion base set 820, 820A-820C, include a number ofdifferent base motion types, each of which is effected by the transportapparatus 306, 311, 400 in a statistically characterizing number ofcommon motions for each base motion type. Each of the different basemotion types has a different corresponding at least one torque commandcharacteristic and position command characteristic defining a differentcommon motion respective to each base motion type. In one aspect, thepredetermined base motion set 820, 820A-820C may be of one or moremove/motion types. For example, the respective moves 501, 502, 503 inthe base motion set 820, 820A-820C may be simple moves or complex (e.g.blended) moves that are characterized by torque and position commandsthat define the respective move.

A simple move is a straight line move between two points (as illustratedin FIG. 5C from point 0 to point 1) or a move in a circular arc betweentwo points (as illustrated in FIG. 5C from point 1 to point 2) along oneof the theta axis, the extension axis or the Z axis of the roboticmanipulator 306, 311, 400 (e.g. a one degree of freedom move).

A complex or blended move is a move in which more than two simple movesare blended together as illustrated in FIG. 5B where the move extendsfrom point 0 to point 2 with a blended path adjacent point 1 that blendsthe two straight line moves from points 0 to 1 and from points 1 to 2along at least two of the theta axis, the extension axis or the Z axisof the robotic manipulator 306, 311, 400 (e.g. a two or more degree offreedom move).

Each of the motion base sets 820, 820A-820C may also be characterized byposition of the moves within the set (e.g. a start and end point of themoves), load parameters of the moves within the set (e.g. the roboticmanipulator 306, 311, 400 is loaded (carrying a substrate) or unloaded(not carrying a substrate)), and/or dynamic conditions at the initialand/or final positions of the move (e.g. motion/stop, stop/stop,stop/motion, motion/motion, etc.). For example, referring to the complexmove in FIG. 5B, the dynamic condition point 0 is stopped and thedynamic condition at point 2 is stopped. Referring to the two simplemoves in FIG. 5C, the dynamic condition at point 0 is stopped and thedynamic condition at point 1 is moving; while the dynamic condition atpoint 2 is stopped. As described above, while the move types have beendescribed with respect to robotic manipulator arm motion in one, two orthree degrees of freedom it should be understood that the move types mayinclude moves generated with any suitable number of degrees of freedomor a single degree of freedom (such as with a vacuum pump, substratealigner, etc.).

Each move type effects the minimum number of moves N_(min) thatstatistically characterizes each move type. For example, each dynamicperformance variable or motion type may be represented in an historicalmanner as:

$\begin{matrix}{{TMAH} = {{{\begin{bmatrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}s_{0} \\ \vdots \end{matrix} \\s_{n}\end{matrix} \\s_{n + 1}\end{matrix} \\ \vdots \end{matrix} \\s_{n + 1 + m_{i}}\end{bmatrix}_{j = 0}^{i = 0}\begin{bmatrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}s_{0} \\ \vdots \end{matrix} \\s_{n}\end{matrix} \\s_{n + 1}\end{matrix} \\ \vdots \end{matrix} \\s_{n + 1 + m_{i}}\end{bmatrix}}_{j = {- 1}}^{i = 0}\begin{bmatrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}s_{0} \\ \vdots \end{matrix} \\s_{n}\end{matrix} \\s_{n + 1}\end{matrix} \\ \vdots \end{matrix} \\s_{n + 1 + m_{i}}\end{bmatrix}}_{j = 0}^{i = 1}\begin{bmatrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}s_{0} \\ \vdots \end{matrix} \\s_{n}\end{matrix} \\s_{n + 1}\end{matrix} \\ \vdots \end{matrix} \\s_{n + 1 + m_{i}}\end{bmatrix}}_{j = {- 2}}^{i = 0}} & (1)\end{matrix}$

where s is a base move/motion signal provided in Table through Table 7.Signals so through s_(n) are signals with a scalar output and should beable to be compared across different template moves (which may also bereferred to as base moves), i.e., compare motor energy relative to thebaseline across different move types. Signals s_(n+1) through s_(n+1+mi)are vector output signals from Table 8 and cannot be compared acrossdifferent template move types, indicated by i.

TABLE 5 Derived signals over base move, per motor, with scalar outputMetric Description Units Peak Tracking error degrees RMS Tracking errordegrees Peak Torque error Newton-meters RMS Torque error Newton-metersPeak Current Amps RMS Current Amps Peak Duty cycle as percentage ofavailable torque Percent Peak Mechanical power Watts Mean Motorefficiency Percent Sum Mechanical energy Joules Mean Winding temperatureCelsius Sum Vibration power, over configurable Hz range dB PeakVibration power dB/Hz Peak Vibration power crest factor dB/Hz

TABLE 6 Derived signals over base move, per arm or end-effector, withscalar output Metric Description Units Condition Peak Settling timeSeconds Fine settling Peak Settling radial error Meters Fine settlingPeak Settling tangential error Meters Fine settling Peak Handoffcompound error Meters At: R EX Change: Z state Peak Tangential trackingerror Meters Change: R state RMS Tangential tracking error MetersChange: R state Peak Composed acceleration, g

TABLE 7 Derived system signals over base move, with scalar output MetricDescription Units Peak Motor bus voltage drop Volts Peak Cumulativemotor mechanical power Watts Sum Cumulative motor mechanical energyJoules

TABLE 8 Derived signals over base move, with vector output DescriptionUnits Position error, per motor Degrees Actual Torque, per motorNewton-meters Tangential error, per arm or end-effector Meters Radialerror, per arm or end-effector Meters

These vector output signals have a signal at each time sample along thetrajectory and therefore the number of these signals differs betweendifferent moves and there is no physical significance to the assessmentat a time sample in one move versus another. The base move (type) indexis indicated by i and the history of a given index is indicated by j.

The last assessed base move in this example is

$\begin{matrix}{{TMAH}_{0} = \begin{bmatrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}s_{0} \\ \vdots \end{matrix} \\s_{n}\end{matrix} \\s_{n + 1}\end{matrix} \\ \vdots \end{matrix} \\s_{n + 1 + m_{i}}\end{bmatrix}_{j = 0}^{i = 0}} & (2)\end{matrix}$

And the 3^(rd) last assessed base move in this example is

$\begin{matrix}{{TMAH}_{- 2} = \begin{bmatrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}s_{0} \\ \vdots \end{matrix} \\s_{n}\end{matrix} \\s_{n + 1}\end{matrix} \\ \vdots \end{matrix} \\s_{n + 1 + m_{i}}\end{bmatrix}_{j = {- 2}}^{i = 1}} & (3)\end{matrix}$

Referring to FIGS. 5A-5C, the base moves, such as base moves 501, 502,503 in the respective set of base moves 820, 820A-820C may also bereferred to as template moves. A base move 501, 502, 503 is a repeatedmove along a unique path. The base moves 501, 502, 503 can be composedof simple moves, or complex moves as described above.

Characteristic data is analyzed along unique path of the base move withrespect to a baseline in order to assess system performance degradationand performance trends. The base move 501, 502, 503 can be definedtheoretically and/or empirically. For example, a theoretical base moveis based on expected design configuration and process of the processtool to resolve expected moves in operation and then executed any time,before or after in situ process tool installation.

An empirical base move may be generated from in situ process movecommands as moves of desired occurrence commonality to generatedsufficient statistical characteristics to have a meaningful statisticalvalue that settles between predefined rate of change convergence boundsas illustrated in FIG. 6 (where N_(min) in FIG. 6 is the minimum numberof moves (e.g. sample size) sufficient to provide a statisticallymeaningful standard deviation base on a given convergence criterion).Generation of the empirical base move may be a two part process (appliedsimilarly to empirical generation of base statistic signature). Forexample, generating the empirical base move may include: accessing an insitu move command histogram 700 (see FIG. 7 ) and identifying in situmoves with commands (e.g. torque, position, boundary parameters, commandtrajectory path (encompasses speed and move duration), load condition,etc.) that map to the base move 501, 502, 503 (e.g. the in situ movematches the base move within a configurable tolerance); and accessingeach dynamic performance variable output by a respective roboticmanipulator 306, 311, 400, for the mapped motion, from a registry 840 ofany suitable registration system 801R, that registers predeterminedoperating data embodying the at least one dynamic performance variableoutput by the robotic manipulator to effect a determination of the otherpredetermined motion set 830 (described below).

The generation of the empirical base move may be performed in near realtime, run in the background and accessing the registry 840 withoutaccessing the controller 319, 323, 422, 423A, 423B, 423C, 800 andassociated bidirectional communication/data channels of the automatedmaterial handling platform 300. The in situ move command histogram 700includes motions commanded by the robotic manipulator controller (suchas controller 319, 323, 422, 423A, 423B, 423C, 800) including in situprocess motions effected by the respective robotic manipulator 306, 311,400. The in situ move command histogram 700 may be registered, in anysuitable registry 700R (see FIG. 8A) of, e.g., the robotic manipulatorcontroller (such as controller 319, 323, 422, 423A, 423B, 423C, 810) orany other suitable controller of the automated material handlingplatform 300. As described herein the robotic manipulator controllerresolves the mapped motions from periodic access of the motion histogram700 in the registry 700R.

For example, referring also to FIG. 8A, a motion resolver 800 resolvesfrom the robotic manipulator 306, 311, 400 (see FIGS. 2 and 3 ) in situprocess motion commands of the robot controller 319, 323, 422,423A-423C, 810, where in situ process motions 501′, 502′, 503′ (see FIG.5A) effected by the transport apparatus map to the predetermined basemotions 501, 502, 503 (each of which defines a corresponding templatemotion so that the in situ process motion map onto the respectivetemplate motions) of a predetermined motion base set (described below),and defines with the mapped in situ process motions 501′, 502′, 503′another predetermined motion set (described below) of the robotcontroller 319, 323, 422, 423A-423C, 810. For example, in situ processmotion 501′ maps to base motion 501, in situ process motion 502′ maps tobase motion 502, and in situ process motion 503′ maps to base motion503. It is noted, in a manner similar to that described above, each insitu process motion 501′-503′ is characterized by at least one of atorque command and a position command from the apparatus controller,where the at least one of the torque command and the position commandcharacterize the in situ process motion in at least one degree offreedom of motion of the robotic manipulator 306, 311, 400.

The motion resolver 800 may be included in the robot controller 319,323, 422, 423A-423C, 810 as a module, the motion resolver 800 may be aremote processor communicably coupled to the robot controller 319, 323,422, 423A-423C, 810, or the motion resolver 800 may be a distinctprocessor communicably linked with the robot controller 319, 323, 422,423A-423C, 810.

The motion resolver 800 iterates through the in situ process moves 501′,502′, 503′ to identify those in situ process moves 501′, 502′, 503′ withthe required minimum number of moves N_(min) as determined by thestandard deviation convergence illustrated in, for example, FIG. 6 . Forexample, as noted above, in order to create a baseline (e.g. establish abase move 501, 502, 503), there must be enough samples collected todefine a statistically meaningful batch. The number of samples requiredto create a baseline depends on the physical nature of the variablebeing analyzed. For instance, it may take longer to define the typical(mean and standard deviation) statistics for a mechanical work of agiven axis of motion of the robotic manipulator 306, 311, 400 than thepeak control torque of the same axis executing the same motion. In orderto remedy this situation, the size of the baseline is defined based onthe statistical analysis of the data collected. For instance, thestandard deviation can be calculated during the baseline data gatheringup to a point where its value stabilizes within some bounds asillustrated in FIG. 6 . In FIG. 6 , the standard deviation of a givenvariable is plotted against the sample size. As the sample sizeincreases, the standard deviation tends to converge within certainbounds. These bounds can be defined a priori or calculated in terms ofthe actual data set, for instance when the rate of change of the plot isunder about +/−10% in variation; however, any suitable convergencemethods and/or percentage of variation can be used.

Referring still to FIG. 8A and FIGS. 5 and 8B, the moves that constituteat least the required minimum number of moves N_(min) (e.g. the movesthat are used to define the baseline) may be referred to as apredetermined motion base set 820. Each of the base moves 501, 502, 503has a respective predetermined motion base set 820A, 820B, 820C that isunique to that base move 501, 502, 503. Exemplary process flows fordetermining and updating the respective predetermined motion base set820A, 820B, 820C are illustrated in FIGS. 8A and 8B.

Still referring to FIGS. 5, 8A and 8B, in one aspect, once the motionresolver 800 identifies and resolves the predetermined motion base set820A, 820B, 820C for a respective base move 501, 502, 503, the in situprocess moves 501′, 502′, 503′ that map (as described above) to arespective one of the base moves 501, 502, 503 are included in therespective predetermined motion base set 820A, 820B, 820C to update therespective predetermined motion base set 820A, 820B, 820C. In otheraspects, the in situ process motions 501′, 502′, 503′ that map to apredetermined motion base set 820A, 820B, 820C of a respective one ofthe base moves 501, 502, 503 may form a different set of motion typesets that is different from the predetermined motion base set 820A,820B, 820C. The updated predetermined motion base set and/or thedifferent set of motion type sets may be referred to as anotherpredetermined motion set 830. As will be described herein, the otherpredetermined motion base set 830A, 830B, 830C for a respective in situprocess move 501′, 502′, 503′ is compared (as described herein) to themotion base set 820A, 820B, 820C for the respective base move 501, 502,503 with respect to the health assessment and predictive diagnostics ofthe automated system being monitored, such as the robotic manipulator300.

As described above, the health assessment of, for example, the roboticmanipulator 306, 311, 400 (or other suitable automated equipment of theautomated material handling platform 300) is performed by generating abase statistic signature (e.g. a baseline or statistical representationof the behavior of a given variable operating in typical environmentalconditions) that characterizes each dynamic performance variable outputby the robotic manipulator 306, 311, 400 for a set of base moves 820,820A, 820B 820C (See FIG. 8A) of the robotic manipulator 306, 311, 400.

In one aspect, baseline metrics are captured/determined, with anysuitable processor 810P (which in one aspect is substantially similar toprocessor 105) of the automated material handling platform 300. Theprocessor 810P may be included in the robot controller 319, 323, 422,423A-423C, 810 as a module, the processor 810P may be a remote processorcommunicably coupled to the robot controller 319, 323, 422, 423A-423C,810 (and motion resolver 800), or the processor 810P may be a distinctprocessor communicably linked with the robot controller 319, 323, 422,423A-423C, 810 (and motion resolver 800). The processor 810P is coupledto the registration system 801R in any suitable manner, while in otheraspects the processor 810P includes the registration system 801R.

The baseline metrics are captured/determined by, for example,calculating the probability density function (PDF) of the base statisticsignature, where the probability function can be represented as:

$\begin{matrix}{{PDF} = {{f\left( {{x❘\mu},\sigma} \right)} = {\frac{1}{\sigma\sqrt{2\pi}}e^{- \frac{{({x - \mu})}^{2}}{2\sigma^{2}}}}}} & (4)\end{matrix}$

where μ is the dataset mean, x is the dynamic performance variable and σis the standard deviation. FIG. 9 shows a typical Gaussian distributionwith a mean and standard deviation. Also defined in FIG. 9 are the upperand lower specification limits (USL and LSL, respectively).

The base statistic signature of each dynamic performance variable of therespective robotic manipulator 306, 311, 400 (see FIGS. 2 and 3 ) isnormalized for each different base move type (move type sets to basevalue) that characterizes the nominal/baseline of each dynamicperformance variable specific to the respective robotic manipulator 306,311, 400 for each different base mote type(s)/move type set(s). Forexample a base value (such as a process capability index C_(pkBase))characterized by the respective probability density function PDF of eachof the dynamic performance variable output by the respective roboticmanipulator 306, 311, 400 for each motion of the predetermined motionbase set is determined.

Generally, the process capability index C_(p)k can be defined as:

$\begin{matrix}{C_{pk} = {\min\left( {\frac{\mu - {3\sigma}}{LSL},\frac{\mu + {3\sigma}}{USL}} \right)}} & (5)\end{matrix}$

where σ is the standard deviation and μ is the mean value of the samplescollected for the respective variable. The process capability indexC_(pk) can be used as metrics to represent a baseline for the respectivedynamic performance variable as the process capability index C_(pk)captures mean and standard deviation of a population sample that islarge enough to provide meaningful statistical data. The upper and lowerspecification limits USL, LSL can be determined in any suitable mannersuch as by defining the upper and lower specification limits USL, LSL asa function of the measured standard deviations of the respective roboticmanipulator 306, 311, 400 being measured. For example:

USL=μ+Nσ  (6)

LSL=μ−Nσ  (7)

where N can be an integer larger than 3 so that the C_(pk) can be anumber larger than 1. As an example, if N=6 then the baseline processcapability index C_(pkBase) can be defined as:

$\begin{matrix}{C_{pkBase} = {\min\left( {\frac{\mu - {3\sigma}}{\mu - {6\sigma}},\frac{\mu + {3\sigma}}{\mu + {6\sigma}}} \right)}} & (8)\end{matrix}$

In one aspect, C_(pkBase) may be set to 2.0 and based theoretically orempirically on the data set mean μ of the baseline being +/−6σ toidentify the upper and lower specification limits USL, LSL so that 99.9%of the sampled moves are captured (as illustrated in FIGS. 9 and 10 ).In other aspects, the upper and lower specification limits USL, LSL maybe configured on a per signal basis when limits are well established,e.g., peak torque limits, maximum settling time, etc.

In one aspect, referring also to FIG. 2A, the corresponding normalizedvalues C_(pkBase(1-n)) and other values C_(pkOther(1-n)) for eachrespective different unique apparatus App1-Appn are registered in anysuitable controller, such as a controller of a respective one of theapparatus App1-Appn. The normalized values C_(pkBase(1-n)) and othervalues C_(pkOther(1-n)), which are uniquely correlated to a respectiveone of the different unique apparatus App1-Appn, are compared todetermine, for each of the different unique apparatus App1-Appn, on anapparatus by apparatus basis, a corresponding performance deteriorationrate (indicated by, for example, a respective linear trending model LTM,see FIG. 11 ) as described in greater detail herein. For example, eachrespective apparatus App1-Appn has a respective linear trending modelLTM1-LTMn as illustrated in FIG. 11 .

Once the baseline metrics is established for each measurement variable(raw and derived), batches of in situ process moves 501′-503′ aresampled during operation of the respective robotic manipulators 306,311, 400. For example, in situ process moves 501′, 502′, 503′ aregenerated by the controller, such as controller 319, 323, 422, 423A,423B, 423C, 810, to identify another statistical signature specific tothe robotic manipulator 306, 311, 400 being monitored. As describedabove, each dynamic performance variable for the set of in situ processmoves are mapped to a respective base move (e.g. a base move type/typeset(s)—see equations 1, 2 and 3). As described above, the mapped in situprocess motions 501′, 502′, 503′ are used to define the otherpredetermined motion set 830, 830A-830C of the respective roboticmanipulator 306, 311, 400.

As with the baseline moves 501-503, the in situ process moves 501′-503′process (another) statistical signature of each dynamic performancevariable of the respective robotic manipulator 306, 311, 400 for eachdifferent in situ (another) move type/type set(s) (e.g. the otherpredetermined motion set 830, 830A-830C) are mapped to a respectivepredetermined motion base set 820, 830A-830C and normalized to an insitu (another) value C_(pkOther) that characterizes the in situperformance of each dynamic performance variable of the respectiverobotic manipulator 306, 311, 400 for each of the different in situ movetypes (which may be simple moves or complex moves). The in situ(another) value C_(pkOther) is a process capability index that ischaracterized by the probability density function PDF of each of thedynamic performance variable output by the robotic manipulator 306, 311,400 effecting the mapped in situ process motions 501′-503′ of the otherpredetermined motion set 830, 830A-830C. The in situ (other) valueC_(pkOther) references the upper and lower limits USL, LSL of thebaseline to position other predetermined motion set relative to thepredetermined motion base set as illustrated in FIG. 10 (where the otherpredetermined motion set is identified as the “new batch” and thepredetermined motion base set is identified as the “baseline”).C_(pkOther) is a process capability index can be defined as:

$\begin{matrix}{C_{pkOthe\tau}^{i} = {\min\left( {\frac{\mu - {3\sigma_{i}}}{LSL},\frac{\mu + {3\sigma_{i}}}{USL}} \right)}_{i}} & (9)\end{matrix}$

where i is an iteration of C_(pkOther) being assessed. The normalized insitu (another) value C_(pkOther) is compared to the normalized basevalue C_(pkBase) for each respective dynamic performance variable beingmonitored, such as for each move type and across move types.

The comparison between the in situ (another) value C_(pkOther) and thebase value C_(pkBase) may be performed by the processor 810P or anyother suitable controller of the automated material handling platform300, where the respective robotic manipulator 306, 311, 400 is a commontransport apparatus for both the predetermined motion base set 820,820A-820C and the other predetermined motion set 830, 830A-830C (and thecorresponding in situ (another) value C_(pkOther) and the base valueC_(pkBase)). The comparison between the in situ (another) valueC_(pkOther) and the base value C_(pkBase) effects a health assessment ofeach dynamic performance variable being monitored for a specificapparatus, such as a respective robotic manipulator 306, 311, 400, byproviding for tracking how much each dynamic performance variabledeviates or drifts from its baseline (see FIG. 10 ). The healthassessment for each of the performance variables can be defined as arelative deviation from its baseline as:

$\begin{matrix}{{Assessment} = {\frac{C_{pkOther}^{i}}{C_{pkBase}} \times 100\%}} & (10)\end{matrix}$

This means that an assessment of 100% represents a perfect statisticalmatch between the in situ (another) value C_(pkOther) and the base valueC_(pkBase). Equation (10) above represents one example of assessment fora given dynamic performance variable. In other aspects, other ways ofmeasuring assessment can be used such as measuring the number ofoccurrences that fall outside the baseline upper and lower limits USLand LSL. FIG. 10 illustrates one example of a health assessmentcalculation for a given dynamic performance variable in terms of itsstatistics. In the example shown in FIG. 10 , 20% of the batch datasamples lie outside the baseline range and the in situ (another) valueC_(pkOther) is penalized.

Still referring to FIG. 10 as well as FIGS. 11 and 12 , the healthassessment of each dynamic performance variable of a respective roboticmanipulator 306, 311, 400 can be defined in terms of the degree in whichthe in situ (another) value C_(pkOther) deviates from the base valueC_(pkBase). The degree of variation can be defined in terms ofprescribed thresholds such as “warning” and “error” where a “warning”may refer to “Attention is Required” and an “error” may refer to“Immediate Action being Required” as will be described below. Anotheraspect of tracking the in situ (another) value C_(pkOther) (and the basevalue C_(pkBase)) is that this tracking provides for trending analysis,i.e. one can estimate or extrapolate when the corresponding dynamicperformance variable is expected to reach the different levels of thedegree of variation.

Determining the amount each dynamic performance variable deviates ordrifts from its baseline provides for trending data TD for each dynamicperformance variable where the trending data TD characterizes adeterioration trend of a respective dynamic performance variable. Thetrending data TD may be registered in any suitable register TDR of theautomated material handing platform 300. FIG. 11 illustrates anexemplary trending data chart of an exemplary dynamic performancevariable; where each assessment A1-An from the comparison of the in situ(another) value C_(pkOther) and the base value C_(pkBase) forpredetermined points in time from different batch samples are plotted onthe chart.

The sloped lines in FIG. 11 represents linear trending models LTM,LTM1-LTMn which can be obtained in any suitable manner such as by usinga Least Squares Method; while in other aspects, any suitable trendingmodel can be used. Trending data characterizing a performancedeterioration trend of, for example, the robotic manipulator 306 (or anyother suitable apparatus of the automated material handling platform 300(see FIG. 2 ) and each of the number different unique apparatusApp1-Appn (see FIG. 2A) of the automated material handling platform 300are registered in a registry of, for example, any suitablecontroller/processor (such as, e.g., a controller of the respectiveapparatus or the tool controller 314 or processor 810P) of the automatedmaterial handling platform 300. In one aspect, the processor 810Pcombines the performance deterioration trends corresponding to thetransport apparatus, such as transport apparatus 306, and each of thenumber of different unique apparatus App1-Appn of the automated materialhandling platform 300 to determine a system performance deteriorationtrend characterizing performance deterioration of the automated materialhandling platform 300.

Referring to the linear trending model LTM, this linear trending modelLTM (which may represent a unique apparatus, such as one of the roboticmanipulator 306, the robotic manipulator 311, the aligner 304, a powersupply PS of the automated material handling platform 300, etc.) can beused—to predict the time t as the estimated time (or cycle) for the warnassessment measure to reach a prescribed warning threshold. Likewise,the time t_(error) can be estimated as the time (or cycle) to reach apoint of where the robotic manipulator 306, 311, 400 operation is notrecommended to continue. As can be seen in FIG. 11 , a linear trendingmodel LTM1-LTMn is determined for each different unique apparatusApp1-Appn. The linear trending models LTM1-LTMn may indicate an overallhealth of the system (such as automated material handling platform 300)as well as the health of each of the different unique apparatusApp1-Appn. Also referring to FIG. 2 , for example, linear trending modelLTM1 may correspond to the power supply PS, linear trending model LTM2may correspond to the robotic manipulator 306, linear trending modelLTM3 may correspond to the robotic manipulator 311 and linear trendingmodel LTMn may correspond to the aligner 307.

As can be seen in FIGS. 11 and 12 the trending data TD may also providefor health assessment warning to be provided to, for example, anoperator of the robotic manipulator 306, 311, 400 through for example,any suitable display 140. For example, any suitable controller of theautomated material handling platform 300, such as processor 810P whichmay be separate from or included in the controller 319, 323, 422, 423A,423B, 423C, 810, may include a trending/assessment unit 870 (FIG. 8A)that is configured to send predetermined signals to indicate to theoperator the health assessment of the robotic manipulator 306, 311, 400.In other aspects, the trending/assessment unit 870 may be part of thecontroller 319, 323, 422, 423A, 423B, 423C, 810. For example, theprocessor 810P may send, or cause to be visually displayed in forexample a yellow color, the “warning” indication when the trending dataTD reaches a first predetermined assessment value WS, the “error”indication may be presented in a red color when the trending data TDreaches a second predetermined assessment value ES (e.g. that is lowerthan the first predetermined assessment value WS), and a “normal”indication (e.g. all dynamic performance variable are withinpredetermined operation limits) may be presented in a green color whenthe trending data is above the first predetermined assessment value WS.In other aspects, the operation status of the automated system (e.g.normal, warning and error) may be presented aurally, visually or in anyother suitable manner.

In one aspect, the processor 810P aggregates dynamic performancevariables, of the at least one dynamic performance variable output bythe transport apparatus, with a highest of the deterioration trends(e.g. the lowest percent assessment) and predicts an occurrence of thetransport apparatus having a performance below predetermined performancestate. For example, the overall health of the robotic manipulator 306,311, 400 can be measured as the worst case assessment across all dynamicperformance variables monitored in a given batch of data samples. Forinstance, if five dynamic performance variables Var1-Var5 (such as,e.g., T1 position actual, Z acceleration actual, bus motor voltage, T2temperature and theta command position to illustrate dissimilarvariables being compared) are measured and compared against theirrespective baseline where the resulting assessment values are:

TABLE 9 Assessment Values Variable Assessment Var1 95% Var2 92% Var3 89%Var4 96% Var5 70%

In the example above, assessment for dynamic performance variable Var5is the lowest assessment of the five dynamic performance variablesVar1-Var5 and can be used to represent the overall current healthassessment of the robotic manipulator 306, 311, 400 whose health ismonitored by all of the five dynamic performance variables Var1-Var5.This can be done independently from the physical nature and meaning ofeach of these dynamic performance variables Var1-Var5 because theassessment can be directly compared across all these entities based onthe fact that the assessments are relative measures against theirrespective baselines.

As an example of the comparison of performance variables describedabove, the processor 810P compares the performance deterioration trendof the transport apparatus 306 with the performance deterioration trendof each of the number of different unique apparatus App1-Appn, anddetermines whether the performance deterioration trend of the transportapparatus 306 or the performance deterioration trend of another of thenumber of different unique apparatus App1-Appn is a controllingperformance deterioration trend and whether a controlling performancedeterioration trend is determinative of performance deterioration trendof the system. For example, at time t_(s) the linear trending model LTM2for the robotic manipulator 306 has the lowest assessment where thislowest assessment is considered the overall health of the automatedmaterial handling platform 300 as described with respect to Table 9. Astime progresses other linear trending models, such as linear trendingmodel LTM1, may show a more rapid performance deterioration rate. Inthis instance, for example, the overall health of the automated materialhandling platform may be judged based on the linear trending model LTM1at, e.g., time to, where a warning is generated based on linear trendingmodel LTM1 at time t_(warnLTM1) and an error is generated based onlinear trending model LTM1 at time t_(errorLTM1).

While the overall health of the automated material handling system maybe determined by a linear trending model having the lowest assessmentvalue for any given time, the linear trending models also provide afingerprint or indication as to which apparatus App1-Appn is the causeor major contributor to the system error or warning. For example, thepower supply PS may affect the other apparatus App1-Appn such as by notsupplying enough voltage to, for example, robotic manipulator 306(corresponding to linear trending model LTM2). As can be seen in FIG. 11, a warning may be generated at time t_(warnLTM1) as a result of thepower supply PS performance deterioration. A warning may be generated attime t_(warnLTM2) for the deterioration in performance of the roboticmanipulator 306; however, the robotic manipulator 306 may be functioningproperly but for the inadequate voltage being supplied to the roboticmanipulator 306 by the power supply PS. These two warnings are anindicator that the power supply PS and the robotic manipulator 306should be checked for repair and suggests that there may be somecorrelation between the deterioration in performance of the power supplyPS and the deterioration in performance of the robotic manipulator 306.

In another aspect, referring to FIGS. 5A and 8A, the aspects of thedisclosed embodiment may provide the health of the system as a combinedaggregate characterization and health prediction. It is noted that thecombined aggregate characterization and health prediction of the systemis different than combining/aggregating the different deteriorationtrends of the components of the system to determine an overall systemdeterioration trend. For example, the combined aggregatecharacterization and health prediction may be considered akin todetermining a deterioration trend of a system having μ number ofdevices, where the system and its multiple devices are treated as asingle unique apparatus, while also determining deterioration trends foreach unique device of the system individually as described above. Inthis aspect, the base moves 501-503 and the in situ process motions501′-503′ are uniquely correlated with a respective unique apparatus asdescribed above. The base moves 501-503 and the in situ process motions501′-503′ may be different for each different apparatus of a common type(e.g. the base moves 501-503 and in situ process motions 501′-503′ ofrobotic manipulator 306 may be different than the base moves 501-503 andin situ process motions 501′-503′ of robotic manipulator 311). The basemotion set 820, 820A-820C and the other predetermined motion set 830,830A-830C for the unique system (such as the automated material handlingplatform 300) may be determined by a base motion set 890 (see FIG. 8A),where the base motions of the base motion set 890 are determined bycombining a number of one or more base motions 501-503 where each of thenumber of one or more base motions is uniquely correlated to a uniquedevice (such as those described in Table 1 and Table 2 above) that iscommunicably connected (e.g. power supply, robotic manipulator, wafersensors, etc.) to form a single aggregate motion 890AG. The singleaggregate motion is uniquely correlated to a unique system (such as theautomated material handling platform 300) and μ number of relatedcombined associated dynamic performance variables (of each deviceoperating in the single aggregate motion) (e.g. S_(0,μ), S_(μ+1)|_(μ)_(devices) ^(i) ^(moves) , where S_(0,μ) is a scalar value and S_(μ+i)is a vector value, so as to generate system performance normalizedvalues C_(pkbase) (system p devices) and for the mapped motions anothervalue C_(pkOther) (system p devices) uniquely related to the system of μnumber of device(s).

In one aspect, referring to FIG. 14 , where a device (such as thoselisted in Tables 1 and 2) is replaced in the system (such as theautomated material handling platform 300) a health determination of thesystem may be generated (FIG. 14 , Block 1400) by repeating the systemhealth determination, where repeating the system health determinationincludes (1) repeating the determination of the deterioration trends (asindicated by the linear trending models LTM, LTM1-LTMn) of each deviceof the system (or at least for the replaced device) and combining thedevice deterioration trends to determine the overall system health froma controlling one of the deterioration trends as described with respectto, e.g., table 9 (FIG. 14 , Block 1401); (2) determining a new systemaggregate deterioration trend of the combined aggregate characterizationas described above (FIG. 14 , Block 1402); (3) identifying if thereplacement device improved or reduced the system overall deteriorationtrend and if the new device reduced the deterioration trend, replace thedevice again, and/or mixing and matching devices to improve the overallsystem deterioration trend (FIG. 14 , Block 1403).

In one aspect weighting of the deterioration trends (FIG. 15 , Block1500) may be applied by any suitable processor of the system (such asthe tool controller 314) to the linear trending models LTM, LTM1-LTMn ofeach device of the system. For example, when applying the weighting thetool controller 314 may determine if the deterioration trend of any oneor more devices is controlling (e.g. the greatest deterioration) orotherwise shows a predicted time of failure outside a predetermined timerange of a desired time to failure (FIG. 15 , Block 1501); or any one ofmore devices may otherwise be identified to be the first devicepredicted to fail and a range (e.g. time range) may be determinedbetween the first device predicted to fail and the last device predictedto fail (FIG. 15 , Block 1502). A history (if any) of past failures mayalso be determined and stored in a memory of the system and reviewed bythe tool controller 314 to determine which devices (if any) are prone tobeing the first to fail (FIG. 15 , Block 1503). From the determinationsmade above, it can be determined by the tool controller 314 if afrequency of failure of a device is inconsistent with the system (e.g.frequencies of failure of other devices) (FIG. 15 , Block 1504). Thetool controller 314 can also identify device characteristics related tosystem performance (e.g., whether the system is operable or inoperablewith the failed device) (FIG. 15 , Block 1505). In one aspect, thedevice characteristics relative to system performance may be categorizedas critical (such as when the system cannot function without the device)or routine (the system can function without the device). The devicecharacteristics may include, but are not limited to the primacy of thedevice, the difficulty in finding a replacement for the device,accessibility of the device within the system (whether the device iseasily accessible for replacement/difficult to access and difficult toreplace), the packaging of the device (e.g. a failed motor in a roboticmanipulator would require replacement of the robotic manipulator while afailed power supply would require replacing only the power supply) orother factors that may affect system downtime and/or replacement deviceavailability.

The weight given to the deterioration trend for each device may bedetermined by, for example, the tool controller 314 based on thefrequency of failure of the device and the device characteristicsrelated to system performance. The weighting to the device deteriorationtrends enhances or discounts the affect the device deterioration trendhas on the deterioration trend of the system overall where the overallsystem health assessment is based on the weighted deterioration trendsof each of the devices of the system.

As a non-limiting example, linear trending models corresponding to adevice that has recently been replaced/repaired may have a lesser weightthan a device that has been in service for some time so that therecently replaced/repaired device has a lesser affect on the overallsystem health determination than the device that has been in service fora longer period of time. In another aspect, the linear trending modelsLTM, LTM1-LTMn may be weighted so that the linear trending models fordevices that are known to fail frequently do not contribute, orcontribute to a limited extent, to the health determination of theoverall system. In other aspects, the health assessment of the systemmay not include any weighting factors applied to the linear trendingmodels LTM, LTM1-LTMn.

Referring now to FIGS. 2, 3, 5A, 8A, 8B and 13 an exemplary healthassessment operation will be described in accordance with the aspects ofthe disclosed embodiment. Predetermined operating data are registered(FIG. 13 , Block 1300) with a registration system 801R communicablycoupled to an apparatus controller 319, 323, 422, 423A-423C, 810. Thepredetermined operating data embody at least one dynamic performancevariable output by the transport apparatus effecting a predeterminedmotion base set 820, 820A, 820B, 820C of predetermined base motions. Abase value C_(pkBase) is determined (FIG. 13 , Block 1310) with, forexample, the processor 810P, communicably coupled to the registrationsystem 801R. The base value C_(pkBase) is characterized by a probabilitydensity function PDF of each of the dynamic performance variable outputby the transport apparatus 306, 311, 400 for each motion of thepredetermined motion base set 820, 820A, 820B, 820C.

Commands for the in situ process motions 501′-503′ are resolved (FIG. 13, Block 1320) by, for example, motion resolver 800 communicably coupledto the apparatus controller 319, 323, 422, 423A-423C, 810. The in situprocess motions 501′-503′ corresponding to the resolved in situ processmotion commands and effected by the transport apparatus 306, 311, 400map to the predetermined base motions 501-503 of the predeterminedmotion base set 820, 820A, 820B, 820C. Another predetermined motion set830, 830A, 830B, 830C of the transport apparatus is defined (FIG. 13 ,Block 1330) with the mapped in situ process motions 501′-503′.

Predetermined operating data embodying the at least one dynamicperformance variable output by the transport apparatus effecting theother predetermined motion set are registered (FIG. 13 , Block 1340) by,for example the registration system 801R. The processor 810P determinesanother value C_(pkOther) (FIG. 13 , Block 1350) that is characterizedby the probability density function PDF of each of the dynamicperformance variable output by the transport apparatus effecting themapped in situ process motions 501′-503′ of the other predeterminedmotion set 830, 830A-830C.

The other value C_(pkOther) and the base value C_(pkBase) are compared(FIG. 13 , Block 1360) by, for example, the processor 810P for each ofthe dynamic performance variable output by the transport apparatusrespectively corresponding to the predetermined motion base set and theother predetermined motion set, wherein the transport apparatus is acommon transport apparatus for both the predetermined motion base setand the other predetermined motion set. The health of the transportapparatus is assessed based on the comparison as described above and anysuitable health assessment notification can be sent to an operator ofthe automated material handling platform 300 as described above.

In accordance with one or more aspects of the disclosed embodiment amethod for health assessment of a system including a transportapparatus:

registering, with a registration system communicably coupled to anapparatus controller, predetermined operating data embodying at leastone dynamic performance variable output by the transport apparatuseffecting a predetermined motion base set of predetermined base motions;

determining with a processor, communicably coupled to the registrationsystem, a base value (C_(pkBase)) characterized by a probability densityfunction of each of the dynamic performance variable output by thetransport apparatus for each motion of the predetermined motion baseset;

with a motion resolver communicably coupled to the apparatus controller,resolving from the transport apparatus in situ process motion commandsof the apparatus controller, where in situ process motions effected bythe transport apparatus map to the predetermined base motions of thepredetermined motion base set, and defining with the mapped in situprocess motions another predetermined motion set of the transportapparatus;

registering, with the registration system, predetermined operating dataembodying the at least one dynamic performance variable output by thetransport apparatus effecting the other predetermined motion set, anddetermining with the processor another value (C_(pkOther)) characterizedby the probability density function of each of the dynamic performancevariable output by the transport apparatus effecting the mapped in situprocess motions of the other predetermined motion set; and

comparing with the processor the other value and the base value(C_(pkBase)) for each of the dynamic performance variable output by thetransport apparatus respectively corresponding to the predeterminedmotion base set and the other predetermined motion set, wherein thetransport apparatus is a unique transport apparatus common for both thepredetermined motion base set and the other predetermined motion set,and assessing the health of the transport apparatus based on thecomparison.

In accordance with one or more aspects of the disclosed embodiment eachof the predetermined base motions defines a template motion and each insitu process motion substantially maps onto a corresponding one of thetemplate motions.

In accordance with one or more aspects of the disclosed embodiment eachtemplate motion is characterized by at least one of a torque command anda position command from the apparatus controller.

In accordance with one or more aspects of the disclosed embodiment theat least one of the torque command and the position command characterizetemplate motion in at least one degree of freedom of motion of thetransport apparatus.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises registering, in a registry of the apparatuscontroller, a histogram of motions commanded by the apparatus controllerincluding in situ process motions effected by the transport apparatus,and wherein the processor resolves the mapped motions from periodicaccess of the motion histogram in the registry.

In accordance with one or more aspects of the disclosed embodiment thepredetermined base motions of the predetermined motion base set includea statistically characterizing number of at least one common base motiondefining a base motion type.

In accordance with one or more aspects of the disclosed embodiment thepredetermined base motions, of the predetermined motion base set,include a number of different base motion types, each of which iseffected by the transport apparatus in a statistically characterizingnumber of common motions for each base motion type.

In accordance with one or more aspects of the disclosed embodiment eachof the different base motion types has a different corresponding atleast one torque command characteristic and position commandcharacteristic defining a different common motion respective to eachbase motion type.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises registering, with the registration system,trending data for each of the dynamic performance variable where thetrending data characterizes a deterioration trend of a respectivedynamic performance variable.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises aggregating, with the processor, dynamicperformance variables, of the at least one dynamic performance variableoutput by the transport apparatus, with a highest of the deteriorationtrends and predicting an occurrence of the transport apparatus having aperformance below predetermined performance state.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises providing to an operator of the transportapparatus, with the processor, an indication of a prediction of theoccurrence of the transport apparatus having a performance belowpredetermined performance state based on the aggregation of the dynamicperformance variables.

In accordance with one or more aspects of the disclosed embodiment amethod for health assessment of a system including a transport apparatusis provided. The method comprises:

registering, with a registration system communicably coupled to anapparatus controller, predetermined operating data embodying at leastone dynamic performance variable output by the transport apparatuseffecting a predetermined motion base set disposed so as to define astatistical characterization of predetermined base motions;

determining with a processor, communicably coupled to the registrationsystem, a normalized value statistically characterizing nominalperformance of each of the dynamic performance variable output by thetransport apparatus for each motion of the predetermined motion baseset;

with a motion resolver communicably coupled to the apparatus controller,resolving from the transport apparatus in situ process motion commandsof the apparatus controller, where in situ process motions effected bythe transport apparatus map to the predetermined base motions of thepredetermined motion base set, and defining with the mapped in situprocess motions another predetermined motion set of the transportapparatus;

registering, with the registration system, predetermined operating dataembodying the at least one dynamic performance variable output by thetransport apparatus effecting the other predetermined motion set, anddetermining with the processor another normalized value statisticallycharacterizing in situ process performance of each of the dynamicperformance variable output by the transport apparatus effecting themapped in situ process motion of the other predetermined motion set; and

comparing with the processor the other normalized value and thenormalized value for each of the dynamic performance variable of thetransport apparatus respectively corresponding to the predetermined basemotion set and the other predetermined motion set, and determining aperformance deterioration rate of the transport apparatus from nominalperformance based on the comparison, wherein the apparatus is unique andeach normalized value (C_(pkBase)) for each predetermined base motion ofthe predetermined motion base set and each other value (C_(pkOther)) foreach mapped in situ process motion of the other predetermined motion setare uniquely correlated with but the unique apparatus, and thedetermined performance deterioration rate correlates uniquely with butthe unique apparatus.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises providing the system with a number of differentunique apparatus connected to each other and the transport apparatus,wherein each different unique apparatus from a number of differentunique apparatus(i) has different corresponding normalized values(C_(pkBase)i) for each base motion of the predetermined base motion setand other normalized values (C_(pkOtheri)) for each mapped in situprocess motion of the other predetermined motion set that uniquelycorrelate to no more than that different corresponding uniqueapparatus(i) from the number of different unique apparatus.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises registering, for each different uniqueapparatus(i), the corresponding normalized values (C_(pkBase)i) andother normalized values (C_(pkOtheri)) uniquely correlated to thatdifferent corresponding unique apparatus(i) with the controllerrespectively coupled to that different corresponding unique apparatus,and determining for each different unique apparatus(i), on an apparatusby apparatus basis, the corresponding performance deterioration rate forthat different unique apparatus(i) from comparison of the uniquelycorrelated normalized values (C_(pkBasei)) and other normalized values(C_(pkOtheri)) of that different unique apparatus(i).

In accordance with one or more aspects of the disclosed embodiment eachdifferent unique apparatus from the number of different unique apparatusis of common configuration with the transport apparatus.

In accordance with one or more aspects of the disclosed embodiment eachdifferent unique apparatus from the number of different unique apparatusis of different configuration from the transport apparatus.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises registering, in a registry of the controllertrending data characterizing performance deterioration trend of thetransport apparatus and each of the number different unique apparatus ofthe system.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises combining with the processor the performancedeterioration trends corresponding to the transport apparatus and eachof the number of different unique apparatus of the system to determine asystem performance deterioration trend characterizing performancedeterioration of the system.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises comparing with the processor the performancedeterioration trend of the transport apparatus with the performancedeterioration trend of each of the number of different unique apparatus,and determining with the processor whether the performance deteriorationtrend of the transport apparatus or the performance deterioration trendof another of the number of different unique apparatus is a controllingperformance deterioration trend and whether a controlling performancedeterioration trend is determinative of performance deterioration trendof the system.

In accordance with one or more aspects of the disclosed embodiment eachof the predetermined base motions defines a template motion and each insitu process motion substantially maps onto a corresponding one of thetemplate motions.

In accordance with one or more aspects of the disclosed embodiment eachtemplate motion is characterized by at least one of a torque command anda position command from the apparatus controller.

In accordance with one or more aspects of the disclosed embodiment theat least one of the torque command and the position command characterizetemplate motion in at least one degree of freedom of motion of thetransport apparatus.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises registering, in a registry of the apparatuscontroller, a histogram of motions commanded by the apparatus controllerincluding in situ process motions effected by the transport apparatus,and wherein the processor resolves the mapped motions from periodicaccess of the motion histogram in the registry.

In accordance with one or more aspects of the disclosed embodiment thepredetermined base motions of the predetermined motion base set includea statistically characterizing number of at least one common base motiondefining a base motion type.

In accordance with one or more aspects of the disclosed embodiment thepredetermined base motions, of the predetermined motion base set,include a number of different base motion types, each of which iseffected by the transport apparatus in a statistically characterizingnumber of common motions for each base motion type.

In accordance with one or more aspects of the disclosed embodiment eachof the different base motion types has a different corresponding atleast one torque command characteristic and position commandcharacteristic defining a different common motion respective to eachbase motion type.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises registering, with the registration system,trending data for each of the dynamic performance variable where thetrending data characterizes a deterioration trend of a respectivedynamic performance variable.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises aggregating, with the processor, dynamicperformance variables, of the at least one dynamic performance variableoutput by the transport apparatus, with a highest of the deteriorationtrends and predicting an occurrence of the transport apparatus having aperformance below predetermined performance state.

In accordance with one or more aspects of the disclosed embodiment themethod further comprises providing to an operator of the transportapparatus, with the processor, an indication of a prediction of theoccurrence of the transport apparatus having a performance belowpredetermined performance state based on the aggregation of the dynamicperformance variables.

In accordance with one or more aspects of the disclosed embodiment anhealth assessing apparatus for assessing a health of a system includinga transport apparatus, the health assessing apparatus comprising:

a registration system communicably coupled to a transport apparatuscontroller of the transport apparatus, the registration system beingconfigured to register predetermined operating data embodying at leastone dynamic performance variable output by the transport apparatuseffecting a predetermined motion base set of predetermined base motions,and

register predetermined operating data embodying at least one dynamicperformance variable output by the transport apparatus effecting anotherpredetermined motion set; and

a motion resolver communicably coupled to the transport apparatuscontroller, the motion resolver being configured to resolve from thetransport apparatus in situ process motion commands of the apparatuscontroller, where in situ process motions effected by the transportapparatus map to the predetermined base motions of the predeterminedmotion base set, and

define with the mapped in situ process motions the other predeterminedmotion set of the transport apparatus; and

a processor communicably coupled to the registration system, theprocessor being configured to determine a base value (C_(pkBase))characterized by a probability density function of each of the dynamicperformance variable output by the transport apparatus for each motionof the predetermined motion base set, and

determine another value (C_(pkOther)) characterized by the probabilitydensity function of each of the dynamic performance variable output bythe transport apparatus effecting the mapped in situ process motions ofthe other predetermined motion set,

compare the other value and the base value (C_(pkBase)) for each of thedynamic performance variable output by the transport apparatusrespectively corresponding to the predetermined motion base set and theother predetermined motion set, and

assess the health of the transport apparatus based on the comparison;

wherein the transport apparatus is a common transport apparatus for boththe predetermined motion base set and the other predetermined motionset.

In accordance with one or more aspects of the disclosed embodiment eachof the predetermined base motions defines a template motion and each insitu process motion substantially maps onto a corresponding one of thetemplate motions.

In accordance with one or more aspects of the disclosed embodiment eachtemplate motion is characterized by at least one of a torque command anda position command from the apparatus controller.

In accordance with one or more aspects of the disclosed embodiment theat least one of the torque command and the position command characterizetemplate motion in at least one degree of freedom of motion of thetransport apparatus.

In accordance with one or more aspects of the disclosed embodiment thetransport apparatus controller comprises a registry the registry beingconfigured to register a histogram of motions commanded by the apparatuscontroller including in situ process motions effected by the transportapparatus, and the processor is further configured to resolve the mappedmotions from periodic access of the motion histogram in the registry.

In accordance with one or more aspects of the disclosed embodiment thepredetermined base motions of the predetermined motion base set includea statistically characterizing number of at least one common base motiondefining a base motion type.

In accordance with one or more aspects of the disclosed embodiment thepredetermined base motions, of the predetermined motion base set,include a number of different base motion types, each of which iseffected by the transport apparatus in a statistically characterizingnumber of common motions for each base motion type.

In accordance with one or more aspects of the disclosed embodiment eachof the different base motion types has a different corresponding atleast one torque command characteristic and position commandcharacteristic defining a different common motion respective to eachbase motion type.

In accordance with one or more aspects of the disclosed embodiment theregistration system is further configured to register trending data foreach of the dynamic performance variable where the trending datacharacterizes a deterioration trend of a respective dynamic performancevariable.

In accordance with one or more aspects of the disclosed embodiment theprocessor is further configured to aggregate dynamic performancevariables, of the at least one dynamic performance variable output bythe transport apparatus, with a highest of the deterioration trends andpredict an occurrence of the transport apparatus having a performancebelow predetermined performance state.

In accordance with one or more aspects of the disclosed embodiment theprocessor is further configured to provide, to an operator of thetransport apparatus, an indication of a prediction of the occurrence ofthe transport apparatus having a performance below predeterminedperformance state based on the aggregation of the dynamic performancevariables.

In accordance with one or more aspects of the disclosed embodiment ahealth assessment apparatus for assessing a health of a system includinga transport apparatus, the health assessing apparatus comprising:

a registration system communicably coupled to a transport apparatuscontroller of the transport apparatus, the registration system beingconfigured to register predetermined operating data embodying at leastone dynamic performance variable output by the transport apparatuseffecting a predetermined motion base set disposed so as to define astatistical characterization of predetermined base motions, and

register predetermined operating data embodying at least one dynamicperformance variable output by the transport apparatus effecting anotherpredetermined motion set;

a motion resolver communicably coupled to the transport apparatuscontroller, the motion resolver being configured to resolve from thetransport apparatus in situ process motion commands of the apparatuscontroller, where in situ process motions effected by the transportapparatus map to the predetermined base motions of the predeterminedmotion base set, and

define with the mapped in situ process motions another predeterminedmotion set of the transport apparatus; and

a processor communicably coupled to the registration system, theprocessor being configured to determine a normalized value statisticallycharacterizing nominal performance of each of the dynamic performancevariable output by the transport apparatus for each motion of thepredetermined motion base set,

determine another normalized value statistically characterizing in situprocess performance of each of the dynamic performance variable outputby the transport apparatus effecting the mapped in situ process motionof the other predetermined motion set,

compare the other normalized value and the normalized value for each ofthe dynamic performance variable of the transport apparatus respectivelycorresponding to the predetermined base motion set and the otherpredetermined motion set, and

determine a performance deterioration rate of the transport apparatusfrom nominal performance based on the comparison;

wherein the transport apparatus is a common transport apparatus for boththe predetermined base motion set and the other predetermined motionset.

In accordance with one or more aspects of the disclosed embodiment eachof the predetermined base motions defines a template motion and each insitu process motion substantially maps onto a corresponding one of thetemplate motions.

In accordance with one or more aspects of the disclosed embodiment eachtemplate motion is characterized by at least one of a torque command anda position command from the apparatus controller.

In accordance with one or more aspects of the disclosed embodiment theat least one of the torque command and the position command characterizetemplate motion in at least one degree of freedom of motion of thetransport apparatus.

In accordance with one or more aspects of the disclosed embodiment thetransport apparatus controller comprises a registry, the registry beingconfigured to register a histogram of motions commanded by the apparatuscontroller including in situ process motions effected by the transportapparatus, and the processor is further configured to resolve the mappedmotions from periodic access of the motion histogram in the registry.

In accordance with one or more aspects of the disclosed embodiment thepredetermined base motions of the predetermined motion base set includea statistically characterizing number of at least one common base motiondefining a base motion type.

In accordance with one or more aspects of the disclosed embodiment thepredetermined base motions, of the predetermined motion base set,include a number of different base motion types, each of which iseffected by the transport apparatus in a statistically characterizingnumber of common motions for each base motion type.

In accordance with one or more aspects of the disclosed embodiment eachof the different base motion types has a different corresponding atleast one torque command characteristic and position commandcharacteristic defining a different common motion respective to eachbase motion type.

In accordance with one or more aspects of the disclosed embodiment theregistration system is further configured to register trending data foreach of the dynamic performance variable where the trending datacharacterizes a deterioration trend of a respective dynamic performancevariable.

In accordance with one or more aspects of the disclosed embodiment theprocessor is further configured to aggregate dynamic performancevariables, of the at least one dynamic performance variable output bythe transport apparatus, with a highest of the deterioration trends andpredict an occurrence of the transport apparatus having a performancebelow predetermined performance state.

In accordance with one or more aspects of the disclosed embodiment theprocessor is further configured to provide, to an operator of thetransport apparatus, an indication of a prediction of the occurrence ofthe transport apparatus having a performance below predeterminedperformance state based on the aggregation of the dynamic performancevariables.

It should be understood that the foregoing description is onlyillustrative of the aspects of the disclosed embodiment. Variousalternatives and modifications can be devised by those skilled in theart without departing from the aspects of the disclosed embodiment.Accordingly, the aspects of the disclosed embodiment are intended toembrace all such alternatives, modifications and variances that fallwithin the scope of the appended claims. Further, the mere fact thatdifferent features are recited in mutually different dependent orindependent claims does not indicate that a combination of thesefeatures cannot be advantageously used, such a combination remainingwithin the scope of the aspects of the invention.

What is claimed is:
 1. A method for health assessment of a systemincluding a transport apparatus, the method comprising: registering,with a registration system communicably coupled to an apparatuscontroller, predetermined operating data embodying at least one dynamicperformance variable output by at least one apparatus component of thetransport apparatus effecting a predetermined velocity command base setof predetermined base velocity commands of the transport apparatus;determining with a processor, communicably coupled to the registrationsystem, a base value characterized by a probability density function ofeach of the dynamic performance variable output by the at least oneapparatus component for each velocity command of the predeterminedvelocity command base set; with a resolver communicably coupled to theapparatus controller, resolving from the at least one apparatuscomponent in situ process velocity commands of the apparatus controller,where in situ process velocities effected by the at least one apparatuscomponent map to the predetermined base velocity commands of thepredetermined velocity command base set, and defining with the mapped insitu process velocities another predetermined velocity command set, ofthe transport apparatus that corresponds to the transport apparatus ofthe predetermined velocity command base set; and registering, with theregistration system, predetermined operating data embodying the at leastone dynamic performance variable output by the at least one apparatuscomponent effecting the other predetermined velocity command set, anddetermining with the processor another value characterized by theprobability density function of each of the dynamic performance variableoutput by the at least one apparatus component effecting the mapped insitu process velocities of the other predetermined velocity command setfor comparison of the other value with the base value and assessing thehealth of the transport apparatus based on the comparison.